Banking

Banking

How data science is helping traditional banking thrive.

Data science in Banking

Banks have a natural affinity and a crescent need for Data Science and Artificial Intelligence applications. According to Forbes, 65% of senior financial management expects positive changes from the use of AI in financial services.

The impact from these applications vary from cost reductions, ability to reply faster and more efficiently to customers, decrease in down-times, etc.

This recent pandemic has brought to our attention that some of these technologies are more crucial than ever, and companies that want to thrive in a post-COVID era must embrace them.

Those technologies are crucial both for the immediate response and in the long term, as banks and society shift to a new normal were the data and technology is even more important.

Customers expect fast and effective answers

Enhance the customer experience in a scalable way using chatbots and automated e-mail analysis. Enabling an effective bank-customer communication with real-time responses and reduce the time for manual and repetitive analysis through an automated process.

Update models to deal with new reality

With the drastic shifts in the economy, previous risk models will not be effective anymore. Therefore, the adaptation and perhaps the creation of the new models that can support data-driven decisions are essential.

Anti-money Laundering

AI-based solutions for AML are known to reduce false-positive rates by up to 60% for individuals and 50% for companies, in addition to increasing true positives by up to 5%. This type of device is essential to prevent banks from being victims of criminals and passively contributing to illegal actions.

Product Recommendations

Help your customers make the most out of your digital channels through automatic recommendations of your most relevant products, services, and functionalities available based on their preferences, offering a personalized omnichannel experience.

Guarantee return on your investment!

Every rose has its thorns. Studies have shown that close to 85% of big data projects end up failing, either because there isn't a clear definition of objectives (or no way to measure them), data and infrastructure problems, or other reasons.

Investing in these technologies is a big step for any company and this initiative should reap its benefits as soon as possible. In order to do that you will need a well-defined long-term plan which requires the expertise of people with over 8 years applying data science to business.